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Border irregularity loss for automated segmentation of primary brain lymphomas on post-contrast MRI

Authors
  • El Jurdi, Rosana
  • Nichelli, Lucia
  • Alentorn, Agusti
  • Vaillant, Ghislain
  • Fu, Guanghui
  • Hoang-Xuan, Khê
  • Houillier, Caroline
  • Lehéricy, Stéphane
  • Colliot, Olivier
Publication Date
Feb 18, 2024
Source
Hal-Diderot
Keywords
Language
English
License
Unknown
External links

Abstract

Unlike for other brain tumors, there has been little work on the automatic segmentation of primary central nervous system (CNS) lymphomas. This is a challenging task due the highly variable pattern of the tumor and its boundaries. In this work, we propose a new loss function that controls border irregularity for deep learning-based automatic segmentation of primary CNS lymphomas. We introduce a border irregularity loss which is based on the comparison of the segmentation and it smoothed version. The border irregularity loss is combined with a previously proposed topological loss to better control the different connected components. The approach is general and can be used with any segmentation network. We studied a population of 99 patients with primary CNS lymphoma. 40 patients were isolated from the very beginning and formed the independent test set. The segmentations were performed on post-contrast T1-weighted MRI. The MRI were acquired in clinical routine and were highly heterogeneous. The proposed approach substantially outperformed the baseline across the various evaluation metrics (by 6 percent points of Dice, 40mm of Hausdorff distance and 6mm of mean average surface distance). However, the overall performance was moderate, highlighting that automatic segmentation of primary CNS lymphomas is a difficult task, especially when dealing with clinical routine MRI. The code is publicly available here: https://github.com/rosanajurdi/LymphSeg.

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